Identification of transcriptome-wide, nut weight-associated SNPs in Castanea crenata

Autor:	Younhee Shin, Mina Choi, Eung-Jun Park, Hyo-Ryeon Lee, Ah-Young Shin, Min-Jeong Kang, Sang-A Lee, Namjin Koo, Yong-Min Kim, Tae-Dong Kim, Sathiyamoorthy Subramaniyam, Dongsoo Kyeong
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	0106 biological sciences 0301 basic medicine Nut False discovery rate Support Vector Machine Genotype Population lcsh:Medicine Single-nucleotide polymorphism Genome-wide association study Fagaceae 01 natural sciences Polymorphism Single Nucleotide Article Plant breeding Machine Learning 03 medical and health sciences Species Specificity Nuts education Castanea crenata lcsh:Science Selection (genetic algorithm) Genetics education.field_of_study Multidisciplinary biology Sequence Analysis RNA lcsh:R biology.organism_classification 030104 developmental biology Phenotype Genetic markers lcsh:Q Transcriptome 010606 plant biology & botany Genome-Wide Association Study
Zdroj:	Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019) Scientific Reports
ISSN:	2045-2322
Popis:	Nut weight is one of the most important traits that can affect a chestnut grower’s returns. Due to the long juvenile phase of chestnut trees, the selection of desired characteristics at early developmental stages represents a major challenge for chestnut breeding. In this study, we identified single nucleotide polymorphisms (SNPs) in transcriptomic regions, which were significantly associated with nut weight in chestnuts (Castanea crenata), using a genome-wide association study (GWAS). RNA-sequencing (RNA-seq) data were generated from large and small nut-bearing trees, using an Illumina HiSeq. 2000 system, and 3,271,142 SNPs were identified. A total of 21 putative SNPs were significantly associated with chestnut weight (false discovery rate [FDR] −5), based on further analyses. We also applied five machine learning (ML) algorithms, support vector machine (SVM), C5.0, k-nearest neighbour (k-NN), partial least squares (PLS), and random forest (RF), using the 21 SNPs to predict the nut weights of a second population. The average accuracy of the ML algorithms for the prediction of chestnut weights was greater than 68%. Taken together, we suggest that these SNPs have the potential to be used during marker-assisted selection to facilitate the breeding of large chestnut-bearing varieties.
Databáze:	OpenAIRE
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